import gym, roboschool
import numpy as np

def relu(x):
    return np.maximum(x, 0)

class SmallReactivePolicy:
    "Simple multi-layer perceptron policy, no internal state"
    def __init__(self, observation_space, action_space):
        assert weights_dense1_w.shape == (observation_space.shape[0], 128)
        assert weights_dense2_w.shape == (128, 64)
        assert weights_final_w.shape  == (64, action_space.shape[0])

    def act(self, ob):
        x = ob
        x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
        x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
        x = np.dot(x, weights_final_w) + weights_final_b
        return x

def demo_run():
    env = gym.make("RoboschoolReacher-v1")
    pi = SmallReactivePolicy(env.observation_space, env.action_space)

    while 1:
        frame = 0
        score = 0
        obs = env.reset()

        while 1:
            a = pi.act(obs)
            obs, r, done, _ = env.step(a)
            score += r
            frame += 1
            still_open = env.render("human")
            if still_open==False:
                return
            if not done: continue
            print("score=%0.2f in %i frames" % (score, frame))
            break

weights_dense1_w = np.array([
[ +0.4244, +0.9150, +0.4075, -0.3415, +0.2841, +1.2984, -1.1950, +0.3478, -0.2900, -0.5474, -0.6919, -0.0781, -0.3051, -0.1247, -1.2280, +1.3602, +0.5159, -0.0384, +0.9156, +0.7306, +0.7552, -1.2306, +0.5731, +1.0769, -1.4851, -0.5160, -0.1528, -1.1702, -0.9395, -0.2198, +1.0057, +0.4758, -0.3467, -0.7215, +0.0270, -0.1783, +0.5018, -0.0374, +0.3162, -0.7540, -0.7853, +0.3160, -0.5434, +1.0894, +0.0252, -0.5503, -0.6377, -0.5087, -1.3066, -0.0181, +0.1280, -0.5365, +0.1945, +0.6695, +0.4415, -0.5713, +0.5592, +0.0113, +0.2797, -0.4546, +0.6477, -0.4467, -0.1189, +0.3242, +0.8866, -0.0667, +0.7124, -0.2570, -0.3260, +1.0482, -0.1004, +1.2418, -0.5713, +0.2118, +0.6877, +0.4365, +0.6648, -0.4594, -0.2603, +0.8333, -0.5834, -0.5376, -0.2444, -0.4736, -0.3648, +0.6698, -0.6180, -0.1128, -0.2978, +0.5107, -0.0221, -0.0113, -0.5551, +0.2489, +0.1407, -1.5845, +0.8667, -0.0565, -0.6969, -0.5912, +0.1687, +0.3143, -0.5432, -0.1897, +0.2881, +0.1026, -0.7445, -0.0120, -0.7738, -0.9807, -0.7489, -0.7462, -0.1743, -0.4479, +0.8495, -0.6832, -0.3081, +0.7694, -0.1342, -0.3655, +0.1726, +0.0730, -0.8439, -0.7802, +0.7100, -0.4464, -0.0556, -0.7924],
[ +0.9326, +0.2284, +1.1964, +0.1644, +0.0427, -0.4895, +0.0548, -0.3968, -0.2320, -0.4283, -0.4832, +1.2609, -1.4943, -0.2343, +0.0621, +0.6559, +0.7225, +1.3727, -0.2447, +0.6175, +0.7933, +0.6919, +0.6977, -0.5205, -0.7748, +0.6718, -0.3063, -0.3960, -0.5412, -0.6486, -0.4834, +0.6139, +0.5834, -0.3662, +0.4179, -0.8836, +0.3455, +1.2419, +0.4806, -0.8112, -0.4965, +0.2562, +0.0715, -0.2628, +0.6579, +0.9553, -1.3136, +0.6776, -0.0568, -0.7111, -0.7416, -0.7171, +0.2146, -0.1325, -0.7764, +0.3793, -0.6369, +0.6183, -0.2306, +0.3858, -0.9653, -1.1587, +0.3387, -0.9115, +0.1126, +0.0586, -0.0487, +0.4489, -0.6579, -0.4769, -0.8756, +0.0104, +0.0738, +0.2845, -0.1682, -0.3482, +0.2842, +0.0851, +0.7722, -0.9764, +0.5166, -0.3120, -0.1425, -0.1057, -0.2205, -0.2066, +0.3578, +0.6250, -1.0784, -1.3145, +1.1148, -0.1740, -0.5297, +0.4241, -1.3394, -0.5156, -0.3682, +0.7179, -0.0454, +1.2953, +0.6632, +1.2693, -0.1437, +0.6453, -0.7857, +0.6698, +0.1106, -0.0819, -0.1839, -0.5196, +0.1718, +0.3607, +0.0251, +0.3432, -0.8322, +0.3024, -0.8394, +0.0061, -0.2089, +0.1170, -0.3126, -0.2511, -0.2177, +0.0102, -0.8054, +0.7688, -0.4939, -0.3376],
[ -0.9298, -0.5109, -0.0159, -0.4436, -0.3293, -0.5066, +0.6147, -0.0988, -1.1031, -0.6091, +0.7904, +0.1301, -0.5635, -0.3528, +1.9272, -0.5007, -0.0976, +0.4015, -0.4192, -0.0711, +0.1581, +1.1316, -0.6583, -1.4582, +1.4634, +0.9577, -0.0701, +1.1809, +1.1925, -0.6270, -0.2451, -0.6709, +0.2986, +0.3425, -0.6582, +0.9103, -0.3029, +1.4712, -0.3707, +0.9011, +0.8346, -0.7021, +1.7469, -1.2014, -0.5185, +1.1923, +1.3489, +1.0331, +0.5225, +0.1439, -1.1216, -0.0471, +0.1379, -0.8567, -1.0278, -0.1078, -0.6244, +0.1257, -1.1808, -0.0455, -0.5429, +0.1095, -0.1576, -1.8496, -1.7936, -0.8421, -1.3212, +0.0528, -0.1025, -1.3486, +1.6412, -0.5388, +0.6869, +0.3229, -1.1349, -0.9473, -0.6449, +0.4476, +0.4400, -1.1256, +0.6121, +1.1385, -0.1959, +0.9523, +0.1529, -1.1314, +0.3952, +0.4343, -0.0335, -0.0263, +0.3361, -0.2803, +0.0235, -0.7212, -0.4684, +1.1182, -1.6406, -0.7170, -0.0567, +0.3964, +1.7398, -0.6251, +1.1665, -1.1265, -0.6615, -0.3309, +1.2569, -0.2194, +1.2214, +1.5724, +1.3808, +0.5935, +0.1273, +2.4130, -1.4168, +1.0131, +0.1479, -1.7153, +0.3697, -0.5306, -0.5828, +0.5460, +2.1058, +0.9925, -1.6960, +0.0129, -0.2319, +2.2220],
[ -0.6437, -0.0133, -1.4519, +0.0213, -0.0375, +2.0787, +0.2487, +0.5783, +0.5989, +0.9345, +0.8770, -1.5919, +2.2845, +0.1305, +0.9473, -0.9805, -1.0958, -0.7215, +0.2667, -0.9913, -1.8447, -0.6433, -0.8633, -0.1474, +1.7471, -0.6955, +0.5090, +0.8446, +0.6317, +0.8487, +0.9446, -0.7480, -0.6389, +1.3776, -1.6627, +0.4958, -1.7330, -1.2839, -0.4489, +1.7380, +1.1162, -0.3172, -0.3653, +0.6483, -1.0760, -0.7763, +1.0580, -0.7914, +0.9056, -0.5566, +1.6148, +0.1522, -0.1782, -1.5099, -0.4818, -0.5366, -0.6876, -0.3357, +1.4669, +0.3520, +1.0845, +0.9391, -1.1904, +0.4894, -1.1770, +0.8190, +1.5165, -0.1320, +0.1012, +0.0788, +0.2876, -1.0054, +0.2876, -1.1868, +0.8242, +1.6352, +0.0723, -1.1930, -1.5786, +1.1037, +0.0647, +0.6016, +0.1378, +0.4897, +0.6461, -0.0560, -0.4220, +0.3152, +0.7953, +2.1388, -1.5577, +0.1561, +1.6296, -0.8989, +0.5886, +0.9569, -0.3317, -0.5705, +0.4138, -1.1019, -0.4160, -0.9196, +0.5731, -0.5019, -0.0145, -0.0367, -0.4709, -0.3151, -0.0272, -0.5344, -0.2979, -0.4274, -0.8717, -0.0293, +0.9176, +0.6579, +0.4452, -1.2306, +0.1665, -0.5446, +0.9559, +0.4746, +0.1191, -0.0832, +0.5437, -1.6453, +0.4509, -0.9421],
[ -0.0705, +0.1150, -0.3800, -0.0540, +0.2354, -0.4324, -0.1672, +0.5665, +0.0765, +0.0510, -0.1882, -0.2038, +0.0100, -0.3107, +0.5430, -0.2038, -0.1178, -0.1537, +0.1864, +0.2092, +0.0591, -0.0439, -0.0894, +0.1538, +0.3148, -0.5624, +0.3475, -0.1226, -0.0038, +0.5489, -0.0237, -0.4954, +0.4286, +0.3554, +0.1339, -0.4951, +0.2696, -0.0268, +0.6317, +0.0626, -0.3500, -0.0055, +0.4171, -0.6347, +0.5927, -0.2121, -0.2379, -0.2483, -0.0184, +0.1312, -0.3363, -0.2376, -0.1515, +0.4265, +0.0809, +0.1147, -0.0584, +0.3268, -0.4089, +0.2260, -0.0001, +0.3213, -0.2090, +0.3878, -0.0112, -0.2524, -0.3922, +0.3466, +0.3980, +0.0814, +0.0337, -0.3263, +0.4784, -0.2592, -0.1891, +0.1072, +0.2243, +0.1224, -0.5478, +0.2158, -0.3407, -0.2452, +0.5686, -0.4891, -0.2319, -0.2658, +0.5262, -0.5736, -0.4732, +0.0353, +0.4150, +0.2436, -0.5778, -0.5520, -0.1183, +0.1849, -0.0532, -0.6023, +0.1941, +0.2544, -0.3760, -0.3566, +0.0251, +0.2710, -0.5509, +0.1601, +0.2433, -0.2503, +0.2636, +0.3408, +0.2360, +0.3000, -0.1683, +0.0553, -0.1073, +0.0473, +0.4856, +0.3684, -0.3381, -0.5218, -0.1129, -0.1712, +0.3598, -0.4852, +0.0123, -0.2466, +0.2086, +0.0845],
[ -0.2703, +0.1525, -0.5971, -0.4081, -0.6582, +0.2734, -0.1608, +0.3682, -0.3537, +0.2883, -0.5445, -0.4088, -0.0495, +0.3507, -0.1535, +0.2812, +0.4075, -0.4400, -0.5001, -0.8070, +0.1036, -0.3395, -0.6732, +0.5526, +0.1852, +0.0075, +0.4991, -0.1231, +0.2365, +0.0315, +0.0104, -0.0142, +0.4491, -0.2525, +0.2090, -0.3139, -0.3682, +0.2402, +0.4302, -0.1102, -0.0151, -0.2132, -0.4625, -0.2971, +0.4292, -0.2941, +0.6682, -0.5053, +0.4144, +0.1630, -0.0871, +0.0197, +0.5538, +0.0038, -0.1330, +0.1802, +0.0043, +0.0632, -0.1869, -0.0785, +0.2610, -0.1252, +0.0299, -0.0083, -0.0511, -0.0866, +0.3326, -0.1412, +0.7765, -0.4873, -0.0001, +0.3318, +0.3000, -0.0305, +0.2214, +0.3589, -0.0414, -0.4798, +0.1572, +0.1107, +0.0501, -0.4709, -0.1852, +0.0234, +0.3141, -0.4437, -0.4506, -0.3346, -0.3483, +0.5400, -0.0455, -0.0772, -0.1134, +0.0966, -0.2763, +0.1603, +0.2863, -0.2958, +0.1095, -0.6671, +0.0224, -0.1053, +0.3698, +0.0691, +0.3813, +0.3413, -0.1004, +0.5813, +0.1227, -0.0346, +0.0590, -0.5572, -0.1564, +0.3683, +0.3590, -0.3752, +0.5705, -0.2942, +0.3432, -0.3240, -0.5064, -0.1398, +0.2348, +0.0360, -0.0646, -0.5472, +0.0228, +0.0550],
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])

weights_dense1_b = np.array([ -0.2584, +0.0467, -0.0725, -0.5362, -0.3518, +0.1121, -0.2958, +0.0465, -0.1310, -0.1889, -0.2442, -0.0237, -0.3064, -0.2700, -0.2658, -0.2390, -0.3105, -0.7025, -0.4271, -0.1971, -0.2466, -0.2564, -0.3609, -0.2215, +0.1055, -0.3614, -0.3481, -0.3399, -0.2547, -0.1846, -0.0073, -0.3436, -0.3538, +0.1344, -0.2362, -0.5113, -0.0988, -0.0919, -0.2246, -0.3544, -0.3339, +0.0749, -0.2508, -0.2790, -0.3366, -0.3252, -0.2840, -0.2652, +0.0410, -0.2248, +0.0179, -0.0986, -0.1657, -0.0758, -0.3583, -0.2426, -0.2965, -0.4054, -0.2635, -0.4346, -0.4710, -0.0764, -0.4682, -0.3345, -0.1379, -0.1689, -0.0047, -0.3862, -0.0660, -0.0216, -0.3237, -0.1849, -0.1340, -0.2563, -0.2778, -0.1729, -0.1852, -0.3351, -0.0356, -0.3313, -0.2186, -0.4859, -0.3196, -0.3322, -0.2751, -0.1704, -0.1165, -0.5591, -0.1914, +0.0175, +0.0536, -0.3713, +0.0385, -0.3812, -0.1470, +0.1968, -0.3045, -0.1719, -0.5477, -0.1915, -0.1869, +0.0567, -0.5134, -0.4929, -0.3206, -0.0233, -0.3637, -0.1696, -0.1204, +0.0294, -0.5006, -0.4318, -0.5429, -0.0834, -0.0443, -0.2279, -0.0244, -0.2325, -0.2879, -0.2388, -0.4383, -0.1028, -0.0770, -0.3581, +0.0004, -0.0441, -0.0036, +0.0555])

weights_dense2_w = np.array([
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])

weights_dense2_b = np.array([ -0.3537, +0.0099, -0.1710, +0.0876, +0.0988, -0.0807, -0.1135, +0.0290, +0.0257, -0.2431, +0.1061, -0.1779, -0.0138, +0.0695, +0.0269, -0.1148, -0.0196, -0.2008, -0.0033, +0.0652, +0.0049, -0.0226, +0.0527, -0.0845, +0.0582, -0.1605, -0.2437, -0.1956, -0.1491, -0.1391, -0.1588, +0.1262, -0.1708, +0.0666, -0.1979, +0.0381, -0.1123, +0.0956, -0.1820, -0.1408, -0.3474, -0.0553, -0.3325, -0.0296, -0.2143, -0.3028, -0.0753, -0.0770, +0.0138, +0.1588, -0.0438, +0.0520, +0.1324, -0.0511, -0.2000, -0.0981, +0.0280, +0.1431, +0.1851, -0.0166, -0.4446, -0.0705, +0.0189, -0.4820])

weights_final_w = np.array([
[ +0.4823, -0.8893],
[ -0.2189, +0.2477],
[ +0.2054, -0.3086],
[ -0.4034, -0.1398],
[ -0.5693, -0.2180],
[ -0.0370, +0.4156],
[ +0.8859, -0.0001],
[ +0.4460, -0.1926],
[ -0.3933, -0.4491],
[ +0.5026, -0.5226],
[ -0.2043, -0.2994],
[ -0.6289, -0.4050],
[ -0.5311, -0.1545],
[ +0.1301, +0.4289],
[ +0.1327, +0.4747],
[ -0.2884, -0.2039],
[ +0.2346, -0.7025],
[ +0.1868, +0.2801],
[ +1.0741, +0.3718],
[ +0.3952, +0.0248],
[ -0.8366, -0.1157],
[ +0.9154, +0.0573],
[ +0.3496, +0.1078],
[ -0.2390, +0.0437],
[ +0.4413, -0.6834],
[ +0.4920, +0.1192],
[ +0.3829, +0.3975],
[ +0.2001, -0.4928],
[ -0.3513, +0.1872],
[ -0.3418, +0.1176],
[ -0.2677, +0.6361],
[ +0.1064, -0.3420],
[ -0.2793, +0.2027],
[ -0.3382, -0.7345],
[ -0.4483, -0.3770],
[ +0.4657, +0.2182],
[ +0.4379, +0.6300],
[ -0.5005, -0.1811],
[ +1.0309, -0.1819],
[ -0.2781, -0.2512],
[ +0.1599, +0.7518],
[ +0.3751, +0.4630],
[ -0.4953, +0.1522],
[ +0.6323, +0.1075],
[ +0.9037, +0.2352],
[ -1.0882, +0.1498],
[ -0.1851, -0.1429],
[ +0.2726, -0.6378],
[ +0.0652, -0.6066],
[ +0.2770, +0.2783],
[ +0.4491, -0.2390],
[ -0.0581, -0.2242],
[ -0.1740, -0.2574],
[ +0.6334, +0.0991],
[ -0.5075, +0.3734],
[ -0.9403, +0.2447],
[ -0.1812, +0.4498],
[ +1.0452, +0.2328],
[ -0.1492, -0.5807],
[ +0.9600, +0.4706],
[ +0.3012, +0.2910],
[ +0.2671, -0.2030],
[ -0.6024, +0.0744],
[ +0.2304, +0.2502]
])

weights_final_b = np.array([ -0.1082, -0.0330])

if __name__=="__main__":
    demo_run()
